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dc.contributor.author
Arias Chao, Manuel
dc.contributor.contactPerson
Arias Chao, Manuel
dc.contributor.dataCollector
Arias Chao, Manuel
dc.contributor.producer
Arias Chao, Manuel
dc.contributor.projectLeader
Fink, Olga
dc.date.accessioned
2020-08-07T13:45:22Z
dc.date.available
2020-08-07T13:23:38Z
dc.date.available
2020-08-07T13:45:22Z
dc.date.created
2018
en_US
dc.date.issued
2020
dc.identifier.uri
http://hdl.handle.net/20.500.11850/430646
dc.identifier.doi
10.3929/ethz-b-000430646
dc.format
text/csv
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc/4.0/
dc.subject
Turbofan engine
en_US
dc.subject
Anomaly detection
en_US
dc.subject
Fault detection
en_US
dc.title
Advanced Geared Turbofan 30k lbf (AGTF30) Dataset
en_US
dc.type
Dataset
dc.rights.license
Creative Commons Attribution-NonCommercial 4.0 International
dc.date.published
2020-08-07
ethz.size
2.97 MB
en_US
ethz.notes
Supplementing the paper: Manuel Arias Chao, Bryan T. Adey and Olga Fink. “Implicit Supervision for Open Set Fault Diagnostics.” ArXiv abs/1912.12502
en_US
ethz.grant
Data-Driven Intelligent Predictive Maintenance of Industrial Assets
en_US
ethz.publication.place
Zurich
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02604 - Inst. für Bau- & Infrastrukturmanagement / Inst. Construction&Infrastructure Manag.::09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02604 - Inst. für Bau- & Infrastrukturmanagement / Inst. Construction&Infrastructure Manag.::09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
en_US
ethz.date.retentionend
indefinite
en_US
ethz.date.retentionendDate
n/a
ethz.grant.agreementno
176878
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
SNF-Förderungsprofessuren Stufe 2
ethz.relation.isSupplementTo
https://arxiv.org/abs/1912.12502
ethz.relation.isSupplementTo
10.3929/ethz-b-000430653
ethz.relation.isCompiledBy
10.3929/ethz-b-000517153
ethz.date.deposited
2020-08-07T13:23:48Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.description.methods
The AGTF30 dataset provides simulated CM data of an advanced gas turbine during flight. The dataset was synthetically generated with the AGTF30 (Advanced Geared Turbofan 30k lbf) dynamical model. Real flight conditions recorded on board of a commercial jet were taken as input to the AGTF30 model. Two stable flying altitudes and a rapid transient maneuver for altitude adaptation were modeled. The labeled dataset consists of multivariate steady-state responses of the AGTF30 model during 10.000s of flight at cruise with a healthy system condition (i.e., V=0). The unlabeled and test datasets contain 17 concatenated time series of model responses resulting from faulty engine conditions. The test set contains fault types affecting components not present in the unlabeled dataset (e.g., V=3, 4, and 17). Each fault corresponds to an individual component fault and has a duration of approx. 200 s. The flight conditions in which faults were induced are randomly assigned from a subset of three operation intervals extracted from the cruise envelope. The unlabeled dataset also includes data from 500 s of operation when the engine is healthy. The unlabeled and test datasets are, therefore, a set of 18 truncated system conditions. No additional noise was added to the model response since the input values are already noisy. The sampling frequency of the simulation is 1Hz.
en_US
ethz.rosetta.installDate
2020-08-07T13:45:54Z
ethz.rosetta.lastUpdated
2022-03-29T02:53:15Z
ethz.rosetta.versionExported
true
ethz.COinS
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